mlsanityData to 7 Oct 2026

Robust Maximum Entropy Behavior Cloning

arXiv 2101.01251 ↗First posted 4 Jan 2021cs.LGcited by 4

On the map: Offline RL and imitation learning from data → Imitation learning methods

Abstract (arXiv)

Imitation learning (IL) algorithms use expert demonstrations to learn a specific task. Most of the existing approaches assume that all expert demonstrations are reliable and trustworthy, but what if there exist some adversarial demonstrations among the given data-set? This may result in poor decision-making performance. We propose a novel general frame-work to directly generate a policy from demonstrations that autonomously detect the adversarial demonstrations and exclude them from the data set. At the same time, it's sample, time-efficient, and does not require a simulator. To model such adversarial demonstration we propose a min-max problem that leverages the entropy of the model to assign weights for each demonstration. This allows us to learn the behavior using only the correct demonstrations or a mixture of correct demonstrations.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Placed in Imitation learning methods, one of 13 directions in Offline RL and imitation learning from data. Builds on 6 of its 27 references that are in the map. First posted on arXiv 4 Jan 2021.

Builds on

6 of 27 references.

Closest ancestors · 2

Same direction, newest first

  1. 2018An Algorithmic Perspective on Imitation Learning16 Nov
  2. 2018Discriminator-Actor-Critic: Addressing Sample Inefficiency and Reward Bias in Adversarial Imitation Learning9 Sep

Other references · 4

  1. 2015Trust Region Policy Optimizationcited by 2,503
  2. 2016Guided Cost Learning: Deep Inverse Optimal Control via Policy Optimizationcited by 400
  3. 2016A Connection between Generative Adversarial Networks, Inverse Reinforcement Learning, and Energy-Based Modelscited by 167
  4. 2016Model-Free Imitation Learning with Policy Optimizationcited by 52

Built on by

Cited by year

Citing papers per year: 2021: 2; 2022: 1; 2023: 0; 2024: 1.

4 papers.

  1. 2021Imitation Learning: Progress, Taxonomies and Challengescited by 56
  2. 2022Discriminator-Guided Model-Based Offline Imitation Learningcited by 17
  3. 2021Multi-Agent Advisor Q-Learningcited by 4
  4. 2024Swarm Behavior Cloningcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2023Unlabeled Imperfect Demonstrations in Adversarial Imitation Learning
  2. 2023Skill Disentanglement for Imitation Learning from Suboptimal Demonstrations
  3. 2023Provably Efficient Adversarial Imitation Learning with Unknown Transitions
  4. 2025Learning What to Do and What Not To Do: Offline Imitation from Expert and Undesirable Demonstrations
  5. 2023Sample-efficient Adversarial Imitation Learning
  6. 2019Imitation Learning from Imperfect Demonstration
  7. 2022Robust Imitation Learning from Corrupted Demonstrations
  8. 2022Robust Learning from Observation with Model Misspecification